JPEG: An Image Compression System

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1 JPEG: An Image Compression System ISO/IEC DIS ITU-T Recommendation T.81 Nimrod Peleg update: Sept. 2005

2 Basic Structure Source Image Data Reconstructed Image Data Encoder Compressed Data Decoder

3 Encoder Structure Source Image Data Compressed Data Encoder Model Descriptors Statistical Model Entropy Encoding Model Parameters Entropy Coding Tables

4 Compressed Data Decoder Structure Reconstructed Image Data Entropy Decoder Statistical Model Descriptors Decoder Model Entropy Coding Tables Model Parameters

5 Image Compression Models A Unit that generates a set of descriptors The simplest model: send the data itself to the entropy encoder: PCM The set of descriptors is all possible values of the samples

6 Compression Models: DPCM A simple predictor: We assume the former sample to predict the current one, and send (to the entropy encoder) the difference between the predicted and real value.

7 Lena: The original

8 Histogram of Lena Matlab: imhist

9 Sample Intensity 1 Line Histogram Sample Position Intensity of difference Difference Sample Position

10 DPCM (Cont d) The better performance of DPCM relative to PCM is due to the high degree of correlation found in most images Note that this model is lossless! Histogram of differences (Lena, one neighbor to the left predictor)

11 DCT Encoder Model Used in Lossy JPEG modes Output is fed to Entropy encoder Source Image DCT Q DC DPCM DC Coefficients To Entropy Coding AC Coefficients

12 DCT Decoder Model Quantization is the principal source of distortion to the reconstructed image. Q is done to each coeff. independently, so it can match the HVS response. DC Coefficients DPCM DC Reconstructed Image AC Coefficients IQ IDCT

13 Block Oriented DCT Reconstruction (A too strong quantization effect) 32x32 block A strong quantization

14 The DCT Image

15 Quantization Color Example Fine quantization Coarse quantization

16 Reconstruction from Fourier Magnitude or Phase Magnitude Phase DFT IDFT with constant Magnitude Original + IDFT with constant Phase

17 Transform Coding - Example block of pixels DCT coefficients

18 Other Compression Models Other models were candidates for JPEG: Block Truncation Coding (BTC) Vector quantization (VQ) Other Transform Coding (TC) schemes Sub-band coding (SBC) Other predictive coding models The DCT model provided (Jan.1988) best average image quality for a given bit-rate

19 Coding Model Zig-Zag scan (instead of Raster scan) achieves longer zero coefficients sequences, after quantization. DC Horizontal Frequency Vertical Frequency

20 DC Zig-Zag example

21 Entropy Encoding/Decoding 2 entropy coding structures used in JPEG: Huffman coding: More than 50 years old (1952) Computationally simpler Implementation simpler Requires known / calculated code tables Arithmetic Coding About 10% higher performance Code book adapts dynamically to coded data IBM Patented... David A. Huffman

22 Huffman Entropy Encoder Statistical model: converts descriptors into symbols, each assigned to particular code word Adapter: responsible for the assignments of code words needed by the encoder Code Tables: can be fixed or adapted to data to improve efficiency by a few percent Note that 1 pass (fixed tables) or 2 passes (adaptive table) are options

23 Huffman Encoder Scheme Descriptors Huffman Statistical Model Symbols S1 a b Huffman Encoder Encoded Data Code words Huffman Adapter b S2 a Huffman Code Table Huffman entropy coding table

24 Huffman Decoder Scheme Encoded data Huffman Decoder Symbols Huffman Statistical Model Descriptors Code words Huffman Code Table Huffman entropy code table

25 Arithmetic Coding One pass adaptive binary coder. Achieves about 10% better compression. More complicated then Huffman. Option to transcoding between the two. Almost not in use in JPEG (used in JBIG) IBM Patent.

26 Lossless Mode Based on DPCM only (without DCT and Q) Poor compression relative to Lossy mode (2:1 Vs. 20:1 for color average image) (Almost) Not in use Changed Since 1997: JPEG-LS

27 Progressive Mode Allows the user to preview a rough version of the image Two or more passes through data Approximation of entire image coded first Finer details are coded with each succeeding scan Decoder follows same order in decoding Identical compression and quality (sometimes even better)

28 The complete Scheme (Lossy) JPEG Base line

Image Compression through DCT and Huffman Coding Technique

Image Compression through DCT and Huffman Coding Technique International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul